Development of a Novel IoT-Based Smart Firefighting Robot for Real-Time Fire Detection and Suppression
Ambafi, J. G.
Electrical & Electronics Engineering Department, Federal University of Technology, PMB 65 Minna, Niger State, Nigeria.
Onuche, O.P.
Electrical & Electronics Engineering Department, Federal University of Technology, PMB 65 Minna, Niger State, Nigeria.
Onuche P U
*
Department of Chemistry, Southern Illinois University, Edwardsville, USA and Department of Quality Control, Dangote Cement Plc Ibese, Nigeria.
Babatunde E.T.
Department of Environmental Sciences, Southern Illinois University, Edwardsville, USA.
Abdullahi A. O.
Department of Polymer and Material Science, Coppin State University, USA.
Osazuwa P.
Illinois Environmental Protection Agency, USA.
Ogunnaike, O.
Illinois Institute of Technology, USA.
Okonkwo U. U.
Department of Geography and GIS, Department of Computer Management and Information Systems, Southern Illinois University Edwardsville, USA.
*Author to whom correspondence should be addressed.
Abstract
Fire outbreaks have long posed significant threats to human life, property, and the environment, often resulting in substantial economic and social losses. In this study, we present the design and implementation of an Internet of Things (IoT)-based fire-fighting robot developed to enhance early fire detection and automated suppression. The system was built around a microcontroller unit and integrates a suite of sensors for autonomous operation, enabling real-time fire detection. A motor driver controls the robot’s mobility, while a relay circuit manages the actuation of a direct current (DC) pump connected to a water jet spray mechanism for extinguishing fires. The robot also supports manual override functionality through a web-based interface accessible via smartphones or personal computers, incorporating a live camera feed for remote navigation and control. The prototype design increased response time by 30%, significantly improving the speed of fire suppression interventions. This improvement directly translated to a higher chance of containing fires before they escalate, reducing potential damage and increasing safety for occupants and first responders. We recommended that future developments expand the system's capabilities to address other classes of fires (e.g., Class B and C), beyond the current focus on Class A fires. Additionally, incorporating cloud-based infrastructure can improve real-time data processing, storage, analytics, and multi-location deployment, enabling smarter and more coordinated fire management.
Keywords: Camera, DC pumping machine, motor driver (L298n), raspberry pi 3b, sensors, web page